Unpacking the week’s market volatility: Market Domination Overtime

On today’s episode of Market Domination Overtime, co-hosts Julie Hyman and Jared Blikre dissect the week’s market action.

Major indexes (^DJI, ^IXIC, ^GSPC) retreated from record highs as Nvidia’s (NVDA) earnings failed to provide the boost to markets investors anticipated. Nvidia announced a 10-for-1 stock split, a move historically associated with positive market momentum.

Charles Schwab head trading and derivatives strategist Joe Mazzola also joins the show to offer his insights into the volatility that markets experienced throughout the week.

This post was written by Angel Smith

Video Transcript

That’s the closing bell on Wall Street and now it’s market domination over time.

Let’s get you up to speed on the action from today’s session.

So if you look at the major averages here, all of them finishing in the green, although I guess we’ll wait for things to settle out.

The dow.

It’s really kind of a photo finish there up just four points on the day.

The S and P 500 up 7/10 of 1%.

The NASDAQ, the winner here and the winner of the week for that matter up, 1.1% helped in part by those results from NVIDIA and the momentum that we’ve seen in technology and uh Jared, it has been an interesting week, right?

Because we thought, you know, NVIDIA was the event of the week, but it really didn’t.

There’s so much more, it didn’t, well, and it didn’t really have much of a ripple effect as much as people might have anticipated.

Well, I think the, the big thing for me was that NVIDIA was not enough to, it was not enough to buoy the, the overall market.

We saw the S and P 500 put in a nasty red candle.

The dow was terrible.

Now, that’s a Boeing story.

But you also saw Goldman Sachs and Sales Force.

So, are there some underlying currents in the market?

The market was reacting to some economic data that came in hotter than expected?

So, arguably, you know, the economic da data was kind of front seat and NVIDIA was a bit of a backseat but, you know, they cleared the hurdle.

So I think that was a big thing.

Exactly.

And I know you’re looking at some of the groups here and what we saw for the week with energy energy was did bad on the wi Fi interactive energy down 3.8%.

So is real estate a very similar amount.

And then tech and communication services, the only spot at green there.

And by the way, just heard that the NA the NASDAQ closed at a record again.

So it explains there that you’ve got those two groups that are the most heavily weighted in the NASDAQ that are doing.

Yes, agreed.

All right, let’s get to that more on that stock split.

You bet NVIDIA this week announcing a 10 for one stock split.

Story continues

It’s the eighth stock split announced by a company so far this year.

And historically, these moves have been bullish for the companies that announced them.

Analysis by Bank of America showed that on average companies announced a stock split, they performed S and P five, they outperformed the S and P 500 index in the following year and it’s not by a little by a lot.

And I was thinking about this, why would this be?

And they break it down by decades?

So it doesn’t matter which decade you look at here.

Uh, stock splits tend to be good for performance.

Why would that be though?

Maybe because that’s a good problem to have if you have to have a stock split, that’s because you’ve been doing well in your stock, the price has gotten a little bit out of range for the everyday ordinary investors.

So I think it’s just the cohort, the group lends itself to that.

I find this a fascinating phenomenon because theoretically, I can today buy 1/10 of a share of NVIDIA, for example, or a quarter of a share of NVIDIA.

But there’s just something psychologically about owning one share.

And obviously, there’s an affordability difference between having a stock that is worth $100 a share and one that I have to pay $1000 a share for.

So you see sort of the psychological importance of it.

What’s interesting to me is that that number is pushed up by the average question a bit because 30% of the time stocks after they split are down 12 months after.

So, you know, it’s still the, it’s still the majority of the time and the majority of the stocks that experience an increase.

But there are, there is a substantial minority that does have declines.

The other thing, interesting thing that Bank of America looked at is that there’s been over time, a dip in the number of splits.

But recently, there’s been a little bit of a resurgence and they pinpoint 36 companies in the S and P 500 that are above $500 a share.

So maybe there would, they would be the next up that are potentially eligible for something like this.

And not only that, there’s a side bet that if a company, a big company that’s getting a lot of headlines, splits its stock, maybe it’s going to join the Dow, you know, something along that line.

That’s not the case here.

We’ve heard no inklings of that, but there’s a lot of speculation, right?

Because the Dow is a price weighted average.

The folks who put it together don’t want to put a very high price stock in there because it’ll really skew the before it was admitted to the dow, they did a stock split.

So that’s where that comes from.

So we’ll see if it ends up happening with NVIDIA.

Well, speaking of NVIDIA as it prepares for its 10 for one stock split, an announcement arriving alongside its blockbuster earnings report.

Yahoo finances, Dan Halley and myself, we had the chance to speak with Ceo Jensen Huang covering all things A I and what is next for the chip giant?

I’m Julie Hyman.

Host of Yahoo Finance’s market domination here with our tech editor Dan Howley NVIDIA has done it again.

The chip giant blowing past analysts expectations in its strong fiscal first quarter data center revenue alone soaring by 427% year over year.

And the company also another bullish sales forecast which shows that A I spending momentum continues apace on top of all that, the company also announced a 10 for one forward stock split and raised its dividend.

Joining us now NVIDIA founder and Ceo Jensen Wang Fresh off the conference call, Jensen.

Welcome.

Thank you so much for being with us.

I’m happy to be here.

Nice to see you guys.

You too.

I wanna start uh with Blackwell, which is your next generation chip.

It’s shipping this year.

You said on the call, you also said on the call, we will see a lot of Blackwell revenue this year.

So if we’re looking at about $28 billion in revenue in the current quarter, and Blackwell is a more expensive product than Hopper the Chip series out.

Now, what does that imply about revenue in the fourth quarter and for the full year?

Well, it should be significant.

Yeah, Blackwell Blackwell and, and as you know, we guide one quarter at a time and uh but what I, what I could tell you about about Blackwell is this, this is, this is uh uh a giant leap in, in um uh in A I and it was designed for trillion parameter A I models.

And this is, as you know, we’re already at two trillion parameters.

Uh models sizes are growing about doubling every six months and the amount of processing uh between the size of the model, the amount of data uh is growing four times.

And so the ability for uh these data centers to keep up with these large models really depends on the technology that we bring, bring to them.

And so the Blackwell is, is uh designed uh also for incredibly fast infer and inference used to be about recognition of things.

But now infer as you know, is about generation of information, generative A I.

And so whenever you’re talking to Chad GP T and is generating information for you or drawing a picture for you or recognizing something and then drawing something for you.

That generation is a brand new uh Infer technology is really, really complicated and requires a lot of performance.

And so Blackwall is designed for large models for generative A I and we designed it to fit into any data center.

And so it’s air cool liquor cool X 86 or this new revolutionary processor we design called Grace Grace Blackwell super chip.

And then um uh you know, supports uh infinite band data centers like we used to, but we also now support a brand new type of data center, Ethernet.

We’re going to bring A I to Ethernet data centers.

So the number of ways that you could deploy Blackwell is way way higher than the than hopper generation.

So I’m excited about that.

II I wanna talk about the, the Infer Jensen, you know, some analysts have brought up the idea that as we move over towards Infer from the, the training that there may be some in house companies uh uh processors from companies that those made from Microsoft, Google Amazon may be more suited for the actual infer I guess.

How does that impact NVIDIA then?

Well, infer it used to be easy, you know, when people started talking about inference, uh generative A I didn’t exist.

And now generative A I is is uh uh of course, is about prediction, but it’s about prediction of the next token or prediction of the next pixel or prediction of the next frame.

And all of that is complicated and, and generative A I is also used for um understanding the con in order to generate the content properly, you have to understand the context and what what is called memory.

And so now the memory size is incredibly large and you have to have a context memory, you have to be able to generate the next token really, really fast.

It takes a whole lot of tokens to make an image, takes a ton of tokens to make a video and takes a lot of tokens to be able to reason about a particular task.

So that it can make a plan.

And so the the the the genera generative A I um era really made inference a million times more complicated.

And as you know, the number of chips that were intended for inference uh kind of kind of fell by the wayside.

And now people are talking about building new chips, you know, the versatility of invidious architecture makes it possible for people to continue to innovate and create these amazing new A is and then now black wall is coming.

So in other words, do you think you still have a competitive advantage even as the market sort of shifts to infer, we have a great position in inference because inference is just a really complicated problem, you know, and the software stack is complicated.

The type of models that people use is complicated.

There’s so many different types.

It’s just gonna be a giant market market opportunity for us.

The vast majority of the world’s infer today as as people are experiencing in their data centers and on the web, a vast majority of the infer today is done on NVIDIA.

And so we, we I expect that to continue.

Um You said on the call a couple of times that you’ll be supply constrained for both hopper and then Blackwell uh chips.

Well, until next year because of the vast demand that’s out there.

Um What can you do about that?

Are there any sort of levers you can pull to help increase supply copper demand grew throughout this quarter after we announced Blackwell.

And so that kind of tells you how much demand there is out there.

People want to deploy these data centers right now.

They want to put our GP US to work right now and start making money and start saving money.

And so, so that that demand is just so strong.

Um you know, it, it’s really important to take a step back and realize that what we build is not a GP U chip, we call it Blackwell and we call it GP U.

But we’re really building A I factories, these A I factories have CP US and GP US and really complicated memory.

The systems are really complicated.

It’s connected by MV link, there’s an MV link switch, there’s in Finan switches in Finan ni and then now we have Ethernet switches and Ethernet N and all of this connected together with this incredibly complicated spine called MV link.

And then the amount of software that it takes to build all this and run all this is incredible.

And so these A I factories are essentially what we build, we build it as a, as a holistic unit as a holistic architecture and platform.

But then we disaggregate it so that our partners could take it and put it into data centers of any kind.

And every single cloud has slightly different architectures and different stacks and our, our stags and our architecture can now deeply integrate into theirs.

But everybody is a little different.

So we build it as an A I factory, we then disaggregate it so that everybody can have A I factories.

This is just an incredible thing and we do this at very hard, very high volume.

It’s just very, very hard to do.

And so every, every component, every, every part of our data center is the most complex computer the world’s ever made.

And so it’s sensible that almost everything is constrained.

Jess, I wanna ask about the uh cloud providers versus the the other industries that you said are, are getting into the, the JA I game or, or getting NVIDIA chips.

You, you had mentioned that uh in uh comments in the actual release and then we heard from uh CFO collect cress, uh that 40% mid 40% of data center revenue comes from those cloud providers as we start to see these other industries open up.

What does, what does that mean for NVIDIA?

Will, will the cloud providers kind of uh shrink, I guess their share?

And then will these other industries pick up where those cloud providers were?

I expect, I expect them both to grow uh a couple of different areas.

Of course, uh the consumer internet service providers this last quarter, of course, uh big stories from meta, the uh the incredible scale that, that um uh Mark is investing in uh Llama two was a breakthrough.

Llama three was even more amazing.

Uh They’re creating models that, that are, that are activating uh large language model and generative A I work all over the world.

And so, so the work that meta is doing is really, really important.

Uh You also saw uh uh Elon talking about uh the incredible infrastructure that he’s building and, and um one of the things that’s, that’s really revolutionary about, about the, the version 12 of, of Tesla’s uh full self driving is that it’s an end to end generative model.

And it learns from watching videos surround video and it, it learns about how to drive uh end to end and generate using generative A I uh uh predict the next the path and the, and the uh how to steer the uh how to understand and how to steer the car.

And so the, the technology is really revolutionary and the work that they’re doing is incredible.

So I gave you two examples.

Uh a start up company that we work with called Recursion has built up a supercomputer for generating molecules, understanding proteins and generating molecules, molecules for drug discovery.

Uh The list goes on, I mean, we can go on all afternoon and, and just so many different areas of people who are, who are now recognizing that we now have a software and A I model that can understand and be learned, learn almost any language, the language of English of course.

But the language of images and video and chemicals and protein and even physics and to be able to generate almost anything.

And so it’s basically like machine translation.

And uh that capability is now being deployed at scale in so many different industries, Jensen.

Just one more quick.

Last question.

I’m glad you talked about um the auto business and and what you’re seeing there, you mentioned that automotive is now the largest vertical enterprise vertical within data center.

You talked about the Tesla business.

But what is that all about?

Is it, is it self driving among other automakers too?

Are there other functions that automakers are using um within data center?

Help us understand that a little bit better.

Well, Tesla is far ahead in self driving cars.

Um but every single car someday will have to have autonomous capability.

Uh It’s, it’s safer, it’s more convenient, it’s more, more fun to drive.

And in order to do that, uh it is now very well known, very well understood that learning from video directly is the most effective way to train these models.

We used to train based on images that are labeled.

We would say this is a, this is a car, you know, this is a car, this is a sign, this is a road and we would label that manually.

It’s incredible.

And now we just put video right into the car and let the car figure it out by itself.

And and this technology is very similar to the technology of large language models, but it requires just an enormous training facility.

And the reason for that is because there’s videos, the data rate of video, the amount of data of video is so so high.

Well, the the same approach that’s used for learning physics, the physical world um from videos that is used for self driving cars is essentially the same um A I technology used for grounding large language models to understand the world of physics.

Uh So technologies that are uh like Sora, which is just incredible.

Um uh and other technologies vo from, from uh uh Google, incredible the ability to generate video that makes sense that are conditioned by human prompt that needs to learn from video.

And so the next generation of A is need to be grounded in physical A I need to be under needs to understand the physical world.

And the on the best way to teach these A is how the physical world behaves is through video, just watching tons and tons and tons of video.

And so the the combination of this multimodality training capability is going to really require a lot of uh computing demand in the years to come Jensen as always super cool stuff and great to be able to talk to you Dan and I really appreciate it, Jensen Wong, everybody, founder and CEO of NVIDIA.

Great to see you guys.

Thank you Memorial Day weekend is here and the unofficial start of the summer usually brings with it a lower volume in the markets.

And despite markets hitting records this week, there are overhangs higher for longer rates and a looming presidential election.

And according to a new survey from Charles, traders are feeling less bullish joining us now is Joe Mazzola.

He’s the head of trading and derivatives specialist at Charles Schwab.

And thank you for joining us today as we head into the weekend.

Kind of a crazy week here.

Just tell me, how are you seeing the markets right now?

So it’s interesting to, to think that we’re, you know, within a stone’s throw of all time highs when uh the volatility we saw at midweek just, you know, I think, shook the markets a little bit but whether it was uh the fed minutes pointing to hire for longer or, you know, even some fed governors wondering if we’re restrictive enough.

Uh And then, you know, you have the PM is that came in a little bit hot and, and then right along with that, you get a Moonshot earnings announcement from NVIDIA, which kind of set the set things straight again.

So a lot happened in the middle of the week.

It kind of feels like we trickled off a little bit into the weekend.

But uh you know, it, it’s a lot for the markets to digest and a lot for traders to digest.

Yeah, and Joe just to put things in perspective for us as well because as you mentioned, we kind of trickled off into the weekend.

It’s a holiday weekend, we’re going into the summer season.

Do weird things happen on Fridays and summer.

It feels like, you know, volume thin and so there sort of some mysterious market movements sometimes.

Yeah.

No, they, they can, uh, you know, one thing I notice for sure from, uh, my old uh option trading days is that the Friday going into a long weekend, you tend to see uh the VICS come down a little bit.

Uh We saw that today trading back down around 12.

Uh That’s just because option traders don’t want to hold the extra day of time decay over the weekend.

So a lot of times you see volatility compress on the Friday before a long weekend.

But that also can mean as you, as you mentioned is, is, is you can see uh declines in volumes and when there’s declines in volumes, that means b as spreads tend to widen out, which means that you can see some, some movement.

And what’s interesting to me kind of given the week that we saw is um like I said, even with all the volatility we saw in midweek, there’s really only maybe one sector and that’s tech that had a good week.

Uh you know, tech was up over around uh 11 and three quarter to 2%.

But for the, for the most part, uh the only other sector that was, that was unchanged was industrials, everything else was down in the week.

And then if you look at the, at the exchange at the indices themselves dow was down quite a bit.

I mean, a lot of that’s off the, off the, the Boeing announcements.

Um But then, you know, you, you saw the, the Russell having a hard time uh holding pace as well too.

So there’s a lot of churn under the surface right now.

A lot of that kind of, you know, gels with what we’ve seen from our, our, our trader survey in, in terms that, you know, they’re not feeling as bullish as they were in the, in, in Q one.

I think there’s a little bit of um I don’t want to say trepidation, but at the same time, a little concern, right, a little consternation as we, as we kind of move into the summer months.

And I think part of it is, you know, it’s really two fold, it’s the, it’s the inflation data that we got back in, in April that uh that, that proved hotter than expected from the, the, the CP I to, you know, uh the PP I uh and then also kind of what we’re seeing from uh market valuation even though earnings were really good.

You know, we had an 80% beat rate.

We’re still a little bit light on the revenues and it’s the guidance going forward that I think is maybe giving, um, investors a little bit of a pause.

So what do you think the next catalyst is we got some earnings reports still trickling in next week.

We have P CE, but if you read CP I and adjust the rings, you can kind of figure out what it already is.

What do you think moves the market next?

Yeah.

No, I, and I think it’s a great point that, uh, you, you, you just alluded to there.

I think one of the diff differences between the CP I and the PC and the P CE is going to be that housing component.

The CP I has got a much higher weight of uh of that housing component built into it than the P CE does.

So, you know, if, if the expectations of that sticky inflation on the service side has been holding up uh that CP I and you know, really from housing, then the expectation should be if you pull that out, uh maybe to not to the same extent in the P ce, maybe you’re seeing a little bit of a, of a, of a move more towards that disinflation than I think the markets are hoping for.

But really, yeah, the catalyst is going to be interesting because like, like I said, we are close to all time highs but what’s going to be that thrust to push us back up?

I think NVIDIA definitely helped this week, but there’s really nothing uh outside of maybe, you know, sales force.

Uh Costco, you might look at next week in terms of to see kind of what the consumer is doing.

Uh I think that’s important because we, you know, we keep hearing this narrative that consumers are getting and that they don’t have the same cash that they did before.

And are they still spending, are, are they moving down?

Right, in terms of uh uh what the products that they’re buying?

I think we started to see that.

But then the durable goods number today, you know, showed that uh it was higher than expected.

And, and, but even even that, even when you look at the durable goods, that that wasn’t a clear sign because we had revisions back for the last number.

So it’s a lot of this kind of choppy back and forth data, whether it’s the soft data that’s coming in less than expected, but the hard data is uh kind of holding firm.

So I, I think there’s a lot to digest and that, you know, to me, once again, when I look at that, that trader survey, that makes a lot of sense to me because there’s just no clear cut path right now.

Well, Joe, one of the catalysts that’s been pretty consistent has been this A I enthusiasm as evidenced by NVIDIA this week.

And I thought it was interesting that in your uh survey, the traders think that A I is crowded.

The trade is crowded but they still like it.

Yeah, I know.

And that’s, I think that’s such a key point because this trader sentiment survey, it’s a survey.

So it’s attitudinal.

But then we also have the Stax report which I, you know, I, which I talked to you guys about earlier uh earlier in the month and the Stax report is the behavioral uh version of it.

So, what are they doing?

versus what are they saying?

So they, even though they might say that, hey, that seems like an overcrowded trade.

The three stocks that they bought the most heavily in that April cycle were a MD.

NVIDIA and S MC I.

So what happened between those three names?

NVIDIA pulled back 10%.

They use that as an opportunity to buy the dip.

A MD and S MC.

I were both down around 20%.

They use that as an opportunity to buy the dip.

Uh So while they might be saying it’s overcrowded, they’re looking for opportunities, they’re picking their spots and they’re being very discerning in when they go ahead and buy those.

They buy those names.

Joe.

Good to see you have a great holiday weekend.

You too.

Thanks so much.

Thanks.

That’ll do it for today’s market domination over time.

Be sure to come back Tuesday at 3 p.m. Eastern for all of your coverage leading up to and after the closing bell.

But don’t go anywhere on the other side.

Of the break.

It’s the latest edition of our new show asking for a trend, stay tuned.